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kategoriler
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alt kategoriler
23.060
terimler
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terimler

Simple Linear Regression

Statistical model that establishes a linear relationship between a single explanatory variable and a continuous target variable in the form Y = β₀ + β₁X + ε.

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Multiple Linear Regression

Extension of linear regression using multiple explanatory variables to predict a continuous target variable according to Y = β₀ + ΣβᵢXᵢ + ε.

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Coefficient of Determination (R²)

Statistical metric ranging from 0 to 1 measuring the proportion of variance of the target variable explained by the regression model.

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Ordinary Least Squares (OLS)

Method for estimating regression parameters by minimizing the sum of squared residuals between observed and predicted values.

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Residuals

Differences between observed values and values predicted by the regression model, representing prediction errors.

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Multicollinearity

Phenomenon where multiple explanatory variables are highly correlated with each other, making coefficient estimation unstable.

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Heteroscedasticity

Violation of the homoscedasticity assumption where the variance of residuals is not constant across the levels of explanatory variables.

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Influential Values

Observations that, if removed from the dataset, would cause substantial changes in coefficient estimates.

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Cook's Distance

Statistical measure that quantifies the influence of an individual observation on the predicted values of a regression model.

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Ridge Regression

Regularization technique that adds an L2 penalty to coefficients to reduce variance and address multicollinearity.

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LASSO Regression

Regularization method using an L1 penalty that can shrink some coefficients to exactly zero, performing variable selection.

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Bias-Variance Tradeoff

Fundamental trade-off between bias error (oversimplification) and variance error (overfitting to training data).

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Gradient Descent

Iterative optimization algorithm that adjusts coefficients to minimize the cost function by moving in the direction of the negative gradient.

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Prediction Interval

Range of likely values containing the future individual observation with a specified confidence level, wider than a confidence interval.

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Significance Test

Statistical test evaluating whether a regression coefficient differs significantly from zero, typically using the t-statistic.

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